Labs

The labs cover selected image processing methods. For each method, the students are provided with a Jupyter notebook that contains a brief explanation and a template for the implementation. The students are tasked to follow the template and complete the implementation.

Some of the labs include a short quiz (see Organization). The quizzes target selected material from the lecture as indicated in the schedule below.

See information about the semester work.

Schedule

Week	Date	Topic	Quiz topics
1	25. 9.	Introduction	no quiz
2	2. 10.	Active contours	(1.1, 1.3)
3	9. 10.	Point distribution models	(2.1, 2.2)
4	16. 10.	SLIC	(3.1, 3.3)
5	23. 10.	Texture features & Graphcut	(4.1, 4.2)
6	30. 10.	Deep learning: segmentation (U-Net)	(5.1, 5.2)
7	6. 11.	Cell detection	(6.1, 6.2)
8	13. 11.	Frangi vesselness filter	(no quiz)
9	20. 11.	Nodule detection	(8.1, 8.2)
10	27. 11.	B-splines	(9.1, 9.2)
11	4. 12.	Registration	(10.1, 10.2)
12	11. 12.	Registration (Simple ITK)	(11.1, 11.2 + B-splines from lecture)
13	28. 12.	Semester work: consultation	(no quiz)
14	8. 1.	Semester work: presentation	(no quiz)

Required software

Although the students can use the lab computers, they are strongly encouraged to use their own devices, which will allow them to work on the assignments at home. The easiest way to install all required software is through the Conda system. Use the Miniconda installer to obtain a Conda installation.

After installing Conda, create a new environment and install the required dependencies:

conda create -n zmo python=3 jupyter matplotlib numpy

To verify the installation, activate the fresh environment

conda activate zmo

and check that the following commands exit successfuly:

python -c "import numpy; import matplotlib"
jupyter notebook --version

It is necessary to activate the Conda environment before using Jupyter. It is, therefore, required to run

conda activate zmo

before working on the assignments.

Table of Contents

Labs

Schedule

Required software